Associate Professor Mashhuda Glencross is Deputy Associate Dean Academic (Students) in the Faculty of Engineering, Architecture and Information Technology (EAIT) at The University of Queensland (UQ). She holds a PhD in Computer Science from The University of Manchester. Her research focuses on computer graphics, human-computer interaction, and their applications to real-world challenges in renewable energy, health, and disaster resilience. She leads projects on visualisation technologies for decision-making during natural disasters and smart grid systems. Education: BSc (Hons) Polymer Science & Engineering, MSc Computer Science, PhD Computer Science (University of Manchester) Industry Experience: Former Technical Product Manager at ARM Ltd, Co-founder of two UK tech startups, Research Director at Pismo Software (VR interior design), and Director of Research at a smart heating company. Her work spans virtual reality (VR) , augmented reality (AR) , and cybersecurity for mixed-reality systems . Recent projects include developing AI tools for education assessment and grid health visualization platforms. She is a Senior Member of the ACM and serves on editorial boards for Computers & Graphics and ACM PACM journals . Awards include the GRSI Best Paper Award 2021 and recognition for contributions to ACM's Europe Council Best Paper Awards . Her research has commercial impact in gaming, visual effects, and mobile technologies. Current initiatives include the Centre for Energy Data Innovation and supervision of students in energy systems and HCI. She advocates for inclusive technology design and resilience engineering.
Dr Bugra Alkan is a Senior Lecturer in Artificial Intelligence and Robotics at the School of Computer Science and Digital Technologies , London South Bank University . With a PhD in Engineering from University of Warwick (2019), he has held postdoctoral positions at University of Bristol and University of Warwick . Academic Rank: Senior Lecturer Institutional Roles: Module Leader for Industrial Cyber-Physical Systems, Artificial Intelligence, Systems Cybersecurity Editorial Roles: Associate Editor for Industrial Robot and Heliyon , Editorial Board Member for MDPI Sensors and Frontier of Industrial Engineering Research focuses on smart manufacturing , collaborative robotics , and industrial AI with emphasis on cyber-physical production systems , digital twins , and Industry 5.0 . Key projects include: Data-driven Configuration Optimisation of CPPS AI-Enabled Digital Life-cycle Management Deep Learning-based Inline Quality Inspection Human-centric Collaborative Assembly 5.0 Scientific contributions include: Best Paper Awards at CIRP CATS 2016 and IATS’13 Over 40 peer-reviewed publications with h-index 16 Editorial leadership in Q1/Q2 journals Teaching responsibilities span artificial intelligence , cyber-physical systems , and statistical modeling at both undergraduate and postgraduate levels. Current research involves EU-funded TWIN-IT-ROMANS project (€1.76M) addressing robotics manufacturing systems.
Milica Todorovic serves as an Associate Professor in the Department of Materials Science within the Faculty of Science and Engineering at the University of Turku (UTU). She leads the Materials Informatics Laboratory (MIL) and heads the Modern Industrial Materials MSc track. Her institutional roles include Vice-director of the Sustainable Materials and Manufacturing (SUSMAT) UTU profiling area, Vice-chair of COST Action CA22154 DAEMON, Vice-leader of the Human-Centric Artificial Intelligence for Sustainable Future (HAIF) Doctoral Training Network, and Co-lead of Finnish Centre for AI (FCAI) Highlight E. Her research integrates artificial intelligence algorithms with first-principles simulations to optimize functional materials and device performance. The MIL group develops data-driven solutions spanning aerosol research, chemical engineering of bio-based materials, and experimental-computational integration. Current projects focus on battery recycling optimization, magnetic materials design, perovskite engineering, and atmospheric aerosol analysis using Bayesian optimization and active learning techniques. Her work demonstrates significant interdisciplinary reach across computational chemistry, nanotechnology, and sustainable engineering. Scientific contributions include: Development of AI workflows for materials discovery Bayesian optimization frameworks for adsorption structure prediction Question answering systems for materials literature mining Data-efficient methods for molecular property prediction Her teaching portfolio includes advanced courses in Data Visualisation and Analysis, Simulations and New Materials, and Machine Learning for Materials Science. She actively contributes to European research networks through DAEMON and FCAI initiatives, driving collaborative efforts in data-driven materials engineering.
Dr. Scott Allan Orr is an Associate Professor of Heritage and Environmental Risk at the UCL Institute for Sustainable Heritage within the Bartlett School of Environment, Energy, and Resources at University College London. He leads the Heritage, Environmental Risk and Data Analytics (HERADA) research group, focusing on data-driven approaches to understanding and managing environmental risks for heritage, with particular emphasis on climate change impacts on the historic environment. His educational background includes: Doctor of Philosophy from University of Oxford (2018) Master of Research from University College London (2015) Bachelor of Applied Science from University of Toronto (2014) Dr. Orr's research employs interdisciplinary data-driven methodologies spanning engineering, architecture, archaeology, computational data science, and conservation. His current work addresses climate change risk assessment for heritage, microclimatic effects of urban greening on historic structures, computational modeling of wind-driven rain, salt crystallization risk assessment, non-destructive moisture measurement in masonry, and leveraging social media data for heritage access. His approach recognizes that heritage conservation challenges require technically sound solutions that also consider social, cultural, political, and economic contexts. His recent publications demonstrate increasing sophistication in data analytics applications for heritage conservation, with particular focus on climate change impacts across multiple scales - from material-level salt crystallization processes to global heritage climatology mapping. The research shows strong integration of computational modeling with empirical field data, reflecting a shift toward predictive and preventive conservation approaches that address both immediate material concerns and long-term climate adaptation strategies. Dr. Orr currently serves as Deputy Director of Education for the Bartlett School of Environment, Energy, and Resources, overseeing doctoral programs across five different PhD streams. Previously, he served as Departmental Graduate Tutor (Research) at the Institute for Sustainable Heritage. He leads several modules on the MSc Sustainable Heritage program including Heritage Data Mapping and Visualisation and Climate Change and Heritage. He leads the interdisciplinary HERADA research group that works closely with heritage sector partners including Historic Environment Scotland, Historic England, the V&A, and the Belgian Royal Institute for Cultural Heritage. The group leverages data analytics approaches to develop practical solutions for real-world heritage problems, demonstrating how heritage can contribute to addressing society's grand challenges including climate change, technological transformations, and social justice.
Michael Ford serves as a Lecturer in Architecture at the University of Technology Sydney (UTS), Faculty of Design and Society, School of Architecture since January 2024. His academic career spans over a decade, including prior roles as Casual Academic at UTS's School of Architecture (2011-2023) and School of Built Environment (2022-present), plus appointments at UNSW Sydney (2022-2023). Concurrently, he maintains active professional practice as Director of Michael Ford Studio and NSW Registered Architect since 2018. His educational background includes: Master of Architecture (University of Technology Sydney, 2011-2014) Bachelor of Design in Architecture (University of Technology Sydney, 2008-2011) Michael's research integrates architectural innovation with emerging technologies, particularly Generative AI for visual communication in design processes. His work bridges Urban Design , Sustainable Architecture , and Interior Design through practical applications in educational settings. Current projects focus on developing student competencies in AI-driven visualization tools while critically evaluating their professional implications in built environment disciplines. His accolades include: Jack Greenland Energy-Efficient Design Prize (2015) Masters of Architecture Graduate of the Year (2015) Byera Hadley Travelling Scholarship (2012) UTS Architecture Award for Design Leadership (2012) Michael leads the teaching grant Supporting student learning with Generative Ai as a visual communication tool (2025), developing curriculum resources across Architecture and Interior subjects. His teaching portfolio encompasses 13+ courses including Architectural Design Studios, Communications and Construction, and Architectural History, with recent focus on AI literacy through structured learning activities and industry collaborations. He actively contributes to academic governance as Visualisation Institute Executive Committee member (2025) and Responsible Academic Officer (2024-2025), while maintaining professional engagement through ARB NSW assessor roles and architectural practice.
Winnie Dankers is a Lecturer in the Department of Systems Engineering & Multidisciplinary Design at the University of Twente. She has been actively involved in academic research since 2010, with a focus on interdisciplinary and systems-oriented approaches to design and engineering challenges. Her work emphasizes practical applications, such as reverse architecting conventional footwear and project-led education in Industrial Design Engineering. Her research interests span multiple disciplines, including systems engineering, multidisciplinary design integration, product development methodologies, and educational technologies. Key areas include requirement specification frameworks, collaborative visualization tools, and the application of behavioral perspectives in design processes. Dankers also explores the interplay between theoretical frameworks and real-world project implementation in industrial design education. Her articles highlight contributions to footwear architecture, information management in product development, and the design of educational programs. She has presented her research at international conferences and contributed to collaborative projects like the Virtual Reality Lab. Notably, she has no listed scientific awards or students at this time. Her advising and grants narrative reflects an absence of formal student supervision details in the provided texts, but her involvement in multidisciplinary research teams and projects is evident. She is associated with the Virtual Reality Lab at the University of Twente, advancing visualization technologies for design and education.
Maxim Van de Wynckel is a Researcher at the Department of Computer Science at Vrije Universiteit Brussel (VUB), working within the Web and Information Systems Engineering (WISE) Lab. He successfully defended his PhD thesis titled "Interoperable and Discoverable Indoor Positioning Systems" on June 30, 2025, under the supervision of Prof. Dr. Beat Signer. His work focuses on developing frameworks and standards for indoor positioning systems that can interoperate across different technologies and platforms. Van de Wynckel earned his Bachelor in Applied Computer Science from Erasmushogeschool Brussels in 2016 with great distinction, receiving the award for best thesis of his class. He continued his academic journey at Vrije Universiteit Brussel, where he obtained his Master of Science in Applied Computer Science in 2019, also with great distinction. His Master's thesis explored Indoor Navigation by Centralized Tracking, which laid the foundation for his PhD research. Dr. Van de Wynckel's research centers on hybrid and indoor positioning systems, with particular emphasis on interoperability and discoverability of positioning technologies. His work spans low-level and high-level positioning techniques, including SLAM and VSLAM implementations, web-based positioning systems, and artificial intelligence applications for indoor positioning. He has made significant contributions to the field through the development of OpenHPS, an open-source hybrid positioning system framework released in 2020, and SemBeacon, a semantic Bluetooth Low Energy specification released in 2023 that enables semantic description of persons, objects, and environments. His publication record demonstrates a strong trajectory in positioning systems research, with a focus on creating interoperable, discoverable, and privacy-preserving solutions. Van de Wynckel has developed ontologies like POSO (Positioning System Ontology) and FidMark (Fiducial Marker Ontology) to standardize representations of positioning data and visual markers. His work increasingly incorporates Solid PODs for decentralized, user-controlled location data storage, addressing critical privacy concerns in positioning technologies. Best thesis of the class of 2016 Best final work of 2013 During his academic career, Van de Wynckel served as a teaching assistant for various bachelor and master courses including Web Technologies, Databases, Next Generation User Interfaces, Advanced Topics in Big Data, Information Visualisation, and Open Information Systems. He was also involved in supervising Bachelor and Master theses related to his research domain. His research has been supported through his affiliation with the WISE Lab at VUB, where he has developed multiple open-source frameworks and datasets that have gained recognition in the positioning systems community. Van de Wynckel is a core contributor to the OpenHPS project, an open-source hybrid positioning system framework that allows developers to create process networks with graph topology to compute the position of persons or assets. The framework supports various positioning methods including Wi-Fi, Bluetooth, and visual positioning, and is designed to run on servers, mobile applications, and embedded systems. His work with the WISE Lab has resulted in numerous datasets, ontologies, and specifications that advance the field of indoor positioning and location-based services.
Peter Forman is an Assistant Professor in Human Geography at Northumbria University's Department of Geography and Environmental Sciences. His research focuses on energy, materialities, urban governance, and infrastructures, particularly the political significance of circulatory systems and gaseous materials. Prior to joining Northumbria in 2020, he held fellowships at Lancaster University, the University of York, and the University of Tübingen. He earned his PhD in Human Geography from Durham University in 2017. Education: PhD (Human Geography, Durham University, 2017), MA (Social Research Methods, Durham University, 2013), BA (Geography, Durham University, 2011). External affiliations include the Royal Geographical Society (2020–2023). Research interests include securing circulations, energy infrastructures, gaseous governance, and materialities in the Anthropocene. His work explores how flows of energy and materials are governed, with a focus on natural gas networks and hydrogen infrastructure in the UK. He also investigates the ethical dimensions of human-environment interactions in ecological crises. Key publications include analyses of UK gas networks, waste management policies, and flexibility in energy systems. His work bridges political geography, environmental humanities, and infrastructure studies. He is an editorial board member of Geo: Geography and Environment and frequently presents at academic conferences on climate governance and energy politics.
Megan Asanza-Grabenbauer, M.Sc. , works at the Institute of Soil Physics and Rural Water Management (BOKU, Vienna). Her research focuses on soil health, carbon dynamics, and climate-resilient agricultural practices. She contributes to projects like Soil health monitoring and Agri-photovoltaic systems . Research Highlights: Soil carbon sequestration modeling Drought resilience in agroecosystems Microbial activity assessment in arable soils Integration of agrivoltaics with soil conservation Education & Training: Supervised theses include Rainwater Management in Grassland and Microbial Dynamics in Soil Health Farming . Projects involve collaborations with Institute of Soil Research and European Commission initiatives like Transformative Living Labs for Soil Health . Advising: Mentored students including Sabine Huber (Doctoral Thesis on microbial carbon cycling) and Klara Margareta Naynar (Master Thesis on grassland drought resilience).
Dr. Siwei Liu is an Assistant Professor at the School of Natural and Computing Sciences, University of Aberdeen, UK. He previously held a postdoctoral position at MBZUAI and completed his PhD with the Terrier team under the supervision of Prof. Iadh Ounis and Prof. Craig Macdonald. He is actively involved in research, teaching, and PhD supervision. His research focuses on advancing artificial intelligence methods, particularly in graph neural networks, large language models, and recommender systems, with applications in bioinformatics, biomedical image analysis, and multi-modal single-cell data. He is a co-founder and main contributor to the open-source Beta-Recsys project, promoting reproducibility and evaluation in recommendation systems. Dr. Liu's recent publications (2020–2025) reflect a strong trajectory in deep learning for biomedical applications and intelligent systems. Key themes include GNNs for gene-disease and RNA-disease association prediction, cold-start recommendation using heterogeneous graphs, pre-training strategies, and hybrid Transformer-Mamba architectures for radiology report generation. His work appears in top-tier venues such as IEEE TPAMI, ACM Transactions, and Briefings in Bioinformatics. He teaches courses in Data Mining and Visualisation and Natural Language Processing, contributing to the education of future AI practitioners. While no specific awards or student names are listed, his active research and leadership in open-source initiatives highlight his growing impact in the AI and biomedical informatics communities.
Waddah Saeed is a Senior Lecturer and Program Lead for the MSc Data Analytics at the School of Computer Science and Informatics, De Montfort University (DMU). He is actively involved in research, teaching, and external academic service, with affiliations including the Institute of Artificial Intelligence (IAI) and the Centre for Computing and Social Responsibility (CCSR). His educational background includes a PhD in Information Technology from Universiti Tun Hussein Onn Malaysia (2019), a Master in Computer Science (Soft Computing), and a BSc in Computer Science. He previously served as a Postdoctoral Research Fellow at the University of Agder, Norway, and as a Lecturer at Asia Pacific University of Technology & Innovation, Malaysia. His research expertise lies in time series analysis and forecasting, machine learning, and explainable AI, with applications in renewable energy and hierarchical forecasting. He is particularly interested in Graph Neural Networks for time series and feature importance in explainable AI models. His teaching includes courses such as Data Mining, Research Methods, Advanced Data Analytics, and Business Intelligence across BSc and MSc programs. He is an active contributor to the academic community, serving as an External Examiner for Abertay University, an Independent Assessor for the University of Nottingham, and an External Academic Advisor for Birmingham City University. He also reviews grants for the Dutch Research Council and acts as a peer reviewer for publishers including Elsevier, Springer, MDPI, Hindawi, and IET. His professional recognitions include: Gold Award (Publication Category), Universiti Tun Hussein Onn Malaysia, 2019 Best Paper Award, 3rd International Conference of Reliable Information and Communication Technology, 2018 Best Paper Award, 2nd International Conference on Soft Computing in Data Science, 2016 He holds the Fellowship of the Higher Education Academy (FHEA) and has earned certifications in university pedagogy and deep learning. He is currently leading internally funded research projects on divergence in explainable AI methods and solar forecasting. He serves as Guest Editor for a Special Issue on Intelligent Energy Forecasting in Applied Sciences (MDPI). Prospective PhD students with funding and aligned research interests are encouraged to contact him.
Jonathan Scott is a Lecturer at The Scott Sutherland School of Architecture and Built Environment, part of Robert Gordon University. The school is renowned for its focus on sustainable and technologically advanced architecture, with a strong emphasis on community-driven design, immersive cities, and energy-efficient construction. The Scott Sutherland School, one of the oldest in the UK, integrates research and practical application, offering courses accredited by RIBA, RICS, and other professional bodies. Its research groups explore topics like Immersive Cities , Circular Economy in Construction , and Pedagogical Innovations , while fostering international collaborations. Students under the school have worked on impactful projects, such as zero-carbon urban corridors, bioluminescent pavilions, and sustainable housing retrofits. Facilities include high-spec IT suites, model-making workshops, and a £16m campus with energy-efficient features.
Professor William Holderbaum is a part-time Professor of Control Engineering at Manchester Metropolitan University. He holds a PhD in Automatic Control from the University of Lille and has held academic roles including Lecturer and Senior Lecturer at the University of Reading. His research focuses on control theory applications in health, energy, and robotics, with contributions to rehabilitation engineering, smart grids, autonomous systems, and wireless power transfer. He has published over 200 papers in leading journals and conferences. Education: PhD in Automatic Control, University of Lille (2001) MSc in Mathematics (Pure), Open University UK BSc (First-class Honours) in Mathematics, Open University UK French qualifications: DEA and Maitrise in Electronics and Control Systems Research Expertise: Professor Holderbaum’s work spans mathematical modeling, hybrid systems, and industrial applications. Notable areas include motion planning for autonomous vehicles, energy storage optimization, and electrical stimulation techniques in rehabilitation. His methodologies, such as Hamiltonian systems on Lie Groups, are internationally recognized. Affiliations and Roles: Editorial Board Member: Journal of Nonlinear Dynamics & Mobile Robotics, Energies, IMA Journal of Mathematical Control and Information, Springer Nonlinear Dynamics EPSRC Peer Review Associate College Member Chair of the IMA ‘Mathematics of Robotics’ Conference Awards and Memberships: Fellow of the Higher Education Academy IEEE Member
Kary Främling is a Professor in Data Science with emphasis on data analysis and machine learning at the Department of Computing Science, Umeå University. He serves as the Head of the Explainable AI (XAI) team and holds the prestigious position of WASP Professor within the Wallenberg AI, Autonomous Systems and Software Program, focusing on making AI systems transparent and understandable to end-users in real-world contexts. Professor Främling's research centers on Explainable Artificial Intelligence (XAI), particularly "outcome explanation" - explaining and justifying AI system results. His foundational work includes the Contextual Importance and Utility (CIU) method developed during his 1991-1996 PhD. His research spans Intelligent products, Internet of Things (IoT), Digital Twin technology, and Systems of Systems, emphasizing that explainability is crucial for ensuring AI remains "humane" - able to communicate its reasoning appropriately for different users and situations. Analysis of his recent publications reveals a strong focus on practical XAI implementations across healthcare, smart buildings, IoT systems, and image classification. His work bridges theoretical AI research with practical tool development (py-ciu, ciu R packages) and application-specific implementations, consistently emphasizing contextually appropriate explanations tailored to specific users and scenarios. His scientific recognition includes: WASP Professor (Wallenberg AI, Autonomous Systems and Software Program) As Head of the XAI team, Professor Främling leads research developing and applying explainability methods to real-world AI systems. He teaches "Data preprocessing and visualisation (5DV217)" and has been featured in ACM Computing Surveys, Springer LNCS, and various IEEE/MDPI publications. His work involves international collaborations across Europe, focusing on making black-box AI models transparent and trustworthy through context-aware explanations. Professor Främling leads the XAI research group at Umeå University, developing and applying the Contextual Importance and Utility method across domains including healthcare diagnostics, smart building energy management, agricultural technology, and affect detection systems. The group collaborates with industry partners to implement explainable AI in practical systems while advancing theoretical foundations of explanation generation.
Dr. Jonathan Davidson is a Senior Lecturer and Deputy School Director of Education at the School of Electrical and Electronic Engineering, University of Sheffield. He holds a PhD and MEng from the same institution. His research focuses on power electronics, including electrical power conversion and impedance sensing/system modeling, with applications in piezoelectric transformers, high-frequency plasma reactors, and waste reduction machinery. He leads a KTP grant (InnovateUK £230k, 2024) for intelligent food waste reduction systems. His teaching emphasizes interdisciplinary engineering, designing programs and lecturing in electrical engineering and design for general, aerospace, and electrical engineering students. He uses diverse pedagogical methods like visualiser notes, videos, and role-play. Education: MEng (Electronic Engineering, 2010), PhD (Electronic and Electrical Engineering, 2015), both from the University of Sheffield. Grants: Notable funding includes FPeT (£600k, 2017), Targeted Waveform Plasma Reactor (£980k, 2019), and InnovateUK (£230k, 2024). Teaching Roles: Deputy Director of Aerospace Engineering, Director of Education for interdisciplinary programs, and currently Deputy Director of Education for the School of Electrical and Electronic Engineering. His research spans power electronics, thermal modeling, and environmental sensing, with collaborations across the faculty. Recent work includes sewage sensing for sediment detection and thermal modeling of power electronics.
